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1: Machine Learning Review
b'Chapter 1: Machine Learning Review'
b'Machine learning \xe2\x80\x93 history and definition'
b'What is not machine learning?'
b'Machine learning \xe2\x80\x93 concepts and terminology'
b'Machine learning \xe2\x80\x93 types and subtypes'
b'Datasets used in machine learning'
b'Machine learning applications'
b'Practical issues in machine learning'
b'Machine learning \xe2\x80\x93 roles and process'
b'Machine learning \xe2\x80\x93 tools and datasets'
2: Practical Approach to Real-World Supervised Learning
b'Chapter 2: Practical Approach to Real-World Supervised
b'Formal description and notation'
b'Data transformation and preprocessing'
b'Feature relevance analysis and dimensionality reduction'
b'Model assessment, evaluation, and comparisons'
b'Case Study \xe2\x80\x93 Horse Colic Classification'
3: Unsupervised Machine Learning Techniques
b'Chapter 3: Unsupervised Machine Learning Techniques'
b'Issues in common with supervised learning'
b'Issues specific to unsupervised learning'
b'Feature analysis and dimensionality reduction'
b'Outlier or anomaly detection'
b'Real-world case study'
4: Semi-Supervised and Active Learning
b'Chapter 4: Semi-Supervised and Active Learning'
b'Case study in active learning'
5: Real-Time Stream Machine Learning
b'Chapter 5: Real-Time Stream Machine Learning'
b'Assumptions and mathematical notations'
b'Basic stream processing and computational techniques'
b'Concept drift and drift detection'
b'Incremental supervised learning'
b'Incremental unsupervised learning using clustering'
b'Unsupervised learning using outlier detection'
b'Case study in stream learning'
6: Probabilistic Graph Modeling
b'Chapter 6: Probabilistic Graph Modeling'
b'Markov networks and conditional random fields'
b'Tools and usage'
7: Deep Learning
b'Chapter 7: Deep Learning'
b'Multi-layer feed-forward neural network'
b'Limitations of neural networks'
8: Text Mining and Natural Language Processing
b'Chapter 8: Text Mining and Natural Language Processing'
b'NLP, subfields, and tasks'
b'Issues with mining unstructured data'
b'Text processing components and transformations'
b'Topics in text mining'
b'Tools and usage'
9: Big Data Machine Learning � The Final Frontier
b'Chapter 9: Big Data Machine Learning \xe2\x80\x93 The Final
b'What are the characteristics of Big Data?'
b'Big Data Machine Learning'
b'Batch Big Data Machine Learning'
appA: Appendix A: Linear Algebra
b'Chapter Appendix A: Linear Algebra'
appB: Appendix B: Probability
b'Chapter Appendix B: Probability'
b'Axioms of probability'
backindex: Appendix C: Index
b'Chapter Appendix C: Index'
Chapter 1. Machine Learning Review
Recent years have seen the revival of artificial intelligence (AI) and
machine learning in particular, both in academic circles and the industry. In
the last decade, AI has seen dramatic successes that eluded practitioners in
the intervening years since the original promise of the field gave way to
relative decline until its re-emergence in the last few years.
What made these successes possible, in large part, was the impetus provided
by the need to process the prodigious amounts of ever-growing data, key
algorithmic advances by dogged researchers in deep learning, and the
inexorable increase in raw computational power driven by Moore's Law.
Among the areas of AI leading the resurgence, machine learning has seen
spectacular developments, and continues to find the widest applicability in an
array of domains. The use of machine learning to help in complex decision
making at the highest levels of business and, at the same time, its enormous
success in improving the accuracy of what are now everyday applications,
such as searches, speech recognition, and personal assistants on mobile
phones, have made its effects commonplace in the family room and the board
room alike. Articles breathlessly extolling the power of deep learning can be
found today not only in the popular science and technology press but also in
mainstream outlets such as The New York Times and The Huffington Post.
Machine learning has indeed become ubiquitous in a relatively short time.
An ordinary user encounters machine learning in many ways in their day-to-
day activities. Most e-mail providers, including Yahoo and Gmail, give the
user automated sorting and categorization of e-mails into headings such as
Spam, Junk, Promotions, and so on, which is made possible using text
mining, a branch of machine learning. When shopping online for products on
e-commerce websites, such as https://www.amazon.com/, or watching
movies from content providers, such as Netflix, one is offered
recommendations for other products and content by so-called recommender
systems, another branch of machine learning, as an effective way to retain
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